A reputation system is a type of collaborative filtering algorithm which attempts to collect, distribute, and aggregate feedbacks about all users’ behavior in an effort to strike a balance between the democratic principles of open publishing and maintaining standards of quality. It is emerging as an increasingly important component of online communities to elicit contributions. Ideas about how reputation system impacts a user’s contribution to an online community (i.e., the eBay community) abound, but without much theorizing. The objective of this thesis is to address this theoretical void. Drawing upon Maslow’s hierarchy of needs, I develop a model to explain the relationship between the effects of a reputation system and contribution of knowledge in online communities. Contribution behavior is modeled as a response to varied individual motivations (including the social need, esteem need, cognitive need, and altruistic need), and these motivations are determined by reputation system design characteristics, that is, reputation and identity management mechanisms. The research model is validated by an experiment with a 2x3 factorial design involving 251 graduate students. A fictitious online community, CityTalk.org, is created for this study. All contents in the community are user-generated and made searchable in public. The empirical results provide very strong support for the model. The theoretical and practical implications of the results are discussed. Given the importance of global knowledge sharing, the findings of this study will be important to inform the web designers on the design of online communities to promote contribution behaviors.